General analytics
AuraScore 79/100

Private Wealth Portfolio Attribution Dashboard Pre-Flight Checklist

Validate multi-currency returns, benchmark synchronization, and fee reconciliations prior to client-facing dashboard publication.

Use this prompt when deploying or refreshing executive and client-facing wealth management analytics dashboards. It guarantees mathematical attribution accuracy, currency translations, and benchmark consistency across portfolios.

Template

Role: Lead Wealth Intelligence Analytics Architect with deep expertise in Brinson-Fachler attribution, multi-currency accounting, and institutional wealth reporting.

Context

  • Portfolio Accounting Source: {{portfolio_accounting_platform}}
  • Benchmark Feeds: {{benchmark_index_feed}}
  • Reporting Cadence: {{reporting_cadence}}
  • Client Tier: {{client_segment_tier}}
  • Attribution Engine: {{attribution_methodology}}
  • Data Governance Tier: {{data_governance_tier}}

Task

Create a comprehensive pre-flight QA checklist to validate data integrity, return calculations, benchmark alignments, and access control policies for investment attribution dashboards serving {{client_segment_tier}} prior to the {{reporting_cadence}} reporting release.

Method

  1. Audit reconciliation feeds between {{portfolio_accounting_platform}} and the BI datamart for pending settlement discrepancies and corporate action adjustments.
  2. Confirm calendar alignment and market holiday conventions between client holdings and {{benchmark_index_feed}}.
  3. Validate mathematical consistency of allocation, selection, and interaction effects generated via {{attribution_methodology}}.
  4. Check currency translation logic, verifying spot and forward rates applied to multi-currency cash balances and hedging overlays.
  5. Verify fee drag calculations, ensuring management, performance, and custody fees are accurately annualized without distortion.
  6. Inspect data security parameters, verifying row-level security and masking policies align with {{data_governance_tier}}.
  7. Test visual rendering, extreme outlier handling, and cross-filter responsiveness across complex composite hierarchies.
  8. Establish final sign-off gates involving portfolio managers, data operations, and performance attribution analysts.

Constraints

  • MUST enforce exact mathematical reconciliation (residual variance < 0.0001%) across {{attribution_methodology}} calculations.
  • MUST NOT permit dashboard publication if any holding lacks pricing confirmation from {{portfolio_accounting_platform}}.
  • Must strictly adhere to the access control standards of {{data_governance_tier}}.
  • Include specific checks for blended and custom composite benchmarks.

Output format

Provide a QA verification checklist arranged in the following sequence:

  1. Accounting Feed & Position Reconciliation (4-5 items)
  2. Performance Calculation & Benchmark Synchronization (5-6 items)
  3. Attribution Methodology & FX Arithmetic Verification (4-5 items)
  4. Security, RLS & Release Authorization Gates (3-4 items) For every item, use: [ ] Check ID & Task | Method of Verification | Tolerance Limit | Escalation Path.

Self-review

  • Are all components of {{attribution_methodology}} rigorously verified?
  • Did I include checks for edge cases in multi-asset holdings for {{client_segment_tier}}?
  • Are tolerance limits mathematically explicit and non-subjective?
AuraScore breakdown
79/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering8/12 · Adequate

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness5/5 · Strong

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

data-analytics
data-general
financial-services
wealth-analytics
portfolio-attribution
bi-dashboards